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Online Image and Reputation

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Online Image and Reputation:
Mitigating Negative Impact of False Reviews on Service Industry Businesses
Texas A&M University-Commerce

Texas A&M University-Commerce
Table of Contents Abstract 3 Chapter 1: Introduction 4 Chapter 2: Review of the Literature 6 Chapter 3: Methodology 11 Chapter 4: Findings 12 Chapter 5: Summary, Conclusions, and Recommendations 14 References 15 Appendix 17

Abstract
Online reviews are becoming an increasingly important component in generating a business’s image and reputation. Service industry businesses, particularly those that rely on positive word-of-mouth, are exposed to the negative influence of fake reviews and false ratings. This paper explores the ways that online reviews are manipulated, highlighting the effects of the social and anonymous nature of the Internet and online review sites. Additionally, this paper examines several methods that can be employed to mitigate the long-term impact of false or manipulated reviews, including a brief review of US law, and the monitoring of review sites to ensure that identifiably false reviews are removed.
Keywords: online ratings, service industry, review manipulation
Online Image and Reputation:
Mitigating Negative Impact of False Reviews on Service Industry Businesses
Chapter 1: Introduction
Background
Businesses, especially service industry businesses like hotels, restaurants, and rental agencies, rely on several methods of reaching out to potential customers and expanding their market base. One avenue that helps persuade a potential customer to use their services over a competitor is through the use of online review and ratings sites like Yelp, Insider pages, and City Search (Malbon, 2013).
Online ratings for service industry businesses differ in several important ways from online reviews applied to retail websites like Amazon, Wal-Mart, Target, or any business that provides an online marketplace where customers can rate products (Pavlou & Dimoka, 2006). In online retail, reviews generally apply to tangible, observable characteristics such as the quality of an item, or quantifiable characteristics, such as timeliness of delivery (Chen & Xie, 2005). For service industry businesses, a large part of what gets rated are intangibles such as customer experience and perceived quality of service, which in turn influences consumers who cannot directly observe these characteristics prior to engaging the business (Aral, 2014). While food service providers or hotels can provide pictures of their primary products, it is more difficult for a plumber or electrician to demonstrate the quality of their service through easily digestible pictures, and in all cases, customer experience is a primary motivator of positive or negative reviews (Dellarocas & Wood, 2008).
Problem Statement
False reports or phony negative reviews and opinions shared through online social media negatively influence potential customers and create an adverse effect on a business.
Purpose of the Study
The study purpose of this research is to examine the factors that affect perception and reaction to a business’ response to false reviews. Common factors that result in reactions toward the positive and negative spectrum will be analyzed for possible replication and duplication in future scenarios.
Study Hypothesis
Business that take a proactive role in online social media and quickly address any false reports or fake reviews will mitigate any negative impact on their consumer base and have a better post-response reception with consumers.

Chapter 2: Review of the Literature
Online Marketplace Reviews
As more consumers enter the online sphere, third party ratings sites like those provided by Yelp, Google Reviews, and CitySearch, have become a popular way to help consumers distinguish between competing businesses (Hennig-Thurau & Walsh, 2004). Reviews provided by consumers have also become an important factor that affects a business’s revenue, particularly small businesses in the service industry (Chen & Xie, 2005).
Robert Moran, president, StrategyOne US, notes that online ratings provide several consumer benefits, but also come with notable risks. As rating sites increase in popularity and scope, corporate control over reputation and image will diminish in favor of opinions generated by consumers, suppliers, and employees (Moran, 2012). While getting direct feedback from customers is widely viewed as a positive aspect of online reviews, Moran notes that there are downsides to granting control of a business’s reputation to online reviewers. He notes that rating systems can be gamed by competing agencies, end users are often fickle and can change their demands faster than a company can reasonable accommodate, and that consumers may become weary of being constantly asked to rate their experience (Moran, 2012).
Power of Online Reputation
The power of online ratings systems is undeniable. Reviews provided by customers who have purchased a product or service help to lower the perceived risk to consumers who are considering a transaction with a business, either online or in person (Verhagen, Meents, & Tan, 2006). Additionally, these rating systems allow a business to differentiate themselves from their competition, and those businesses who maintain positive and reputable ratings can result in price premiums (Pavlou & Dimoka, 2006). The ability to create these price premiums is a critical factor in the online business sphere, as it allows for quality service providers an incentive to remain online, and helps prevent low quality vendors from flooding the marketplace, resulting in what Pavlou and Dimoka refer to as a “lemon market” (Pavlou & Dimoka, 2006).
Review Trust and Credibility
In order for online ratings to be effective, there needs to be an inherent trust in the content that is put forth by reviewers. A study that surveyed more than 28,000 Internet users concluded that consumer provided reviews are the second most-trusted source of information regarding online businesses, after word-of-mouth reviews from family and friends (Malbon, 2013). It has also been shown that ratings and reviews, provided by what are assumed to be unpaid users, are viewed by consumers to be more trust-worthy than materials or responses generated by a business, who has a clearly vested interest in presenting only favorable information (Bell, 2009).
However, there are issues with the trust and weight that consumers put in online reviews and ratings. Fake reviews, manipulated ratings, and gaming techniques are eroding the sense of trust that consumers place in online review sites. Some online commentators note that fake reviews are so prevalent and sophisticated that they are difficult to identify and render consumer reviews largely ineffective (Malbon, 2013). The anonymous and impersonal nature of many online review sites makes trust in user provided feedback essential when evaluating a business (Pavlou & Dimoka, 2006). When this trust is eroded, the buyer-seller feedback system becomes ineffective, and introduces increased risk on the part of the consumer, which stifles the online marketplace.
Natural Review Bias
Natural review bias occurs as a result of having voluntary reviews for products and services. This is meaningful for businesses and consumers who view online ratings, as not every customer will review their experience, and generally, only exceptional transactions get reviewed. The literature is compelling on this point, demonstrating that rating systems report overwhelmingly positive reviews (Aral, 2014; Dellarocas & Wood, 2008). Dellacross and Wood note that satisfied customers are more likely to leave a positive rating or review online, while dissatisfied customers are more reserved and prefer to abstain from online reviews. Social influence also plays a factor, as consumers who have a negative experience may fear reprisal or attacks if their review or rating is traced back to them (Aral, 2014).
Interestingly, research suggests that this negative social pressure is taken into account by consumers, and that when a negative review or rating is encountered, consumers will give more weight to the negative information (Kwon & Sung, 2012, p. 208). Research also suggests that extreme ratings are discounted by consumers, and are generally considered less helpful than more moderated reviews (Kwon & Sung, 2012, p. 207).
Online Reputation and Game Theory
Those businesses that can build and control their online reputation over a long period have better performance and enjoy better returns than businesses that are at the mercy of their reviewers (Dellarocas, 2003). It may seem obvious, but it is worth noting that if a business has a long history of providing a highly rated service, consumers will trust that this trend will continue into the future. Therefore it is imperative that businesses that are entering the marketplace tightly control their online reputation at the outset, as positive past performance will tend to generate positive ratings in the future (Aral, 2014).
Online Review Manipulation
Reviews and ratings can be manipulated largely due to economic theory. This theory predicts that voluntary reviews and feedback will be sparse in number due to the fact the previous customers get no direct benefit from the review, and that any review that a customer leaves exposes them to critique and attack from other, future reviewers (Dellarocas, 2003). This theory is borne out particularly for new products, services, or businesses. Conversely, once a product or business has been well rated, the social influence of those ratings will influence other commentators through a selection effect, and this trend will continue over a long period (Aral, 2014).
There are many ways in which reviews can be manipulated, and the Internet contains many advertisements for agencies or individuals who offer fake reviews and ratings in exchange for payment (Malbon, 2013). Additionally, businesses may offer an incentive to leave a positive review by offering a free product or service (Aral, 2014; Malbon, 2013). Such manipulations are both commonplace, and difficult to detect due to the nature and relative anonymity of online review sites.
Lim, et al (2010) noted the following:
Due to the openness of product review sites, spammers can pose as different users (known as ‘sockpuppeting’) contributing spammed reviews making them harder to eradicate completely. Spam reviews usually look perfectly normal until one compares them with other reviews of the same products to identify review comments not consistent with the latter (p. 940)
Mitigation of Manipulated Reviews
Fake reviews or false ratings can be mitigated by a business in many ways. Some fall within the direct influence of the business itself, while others rely on improvements or changes to the online ratings sites. An example of the latter would be for online ratings sites to ensure that reviewers of a product or service can only provide feedback if they provide proof of their purchase (Aral, 2014).
Relying on laws is also an option, although one of limited effectiveness. The US Federal Trade Commission has formal guidelines regarding online reviews, and in summary state that reviews must be honest and disclose any relationship between the reviewer and the business, and that online ratings sties must police their content and eliminate any deceptive reviews (Forrest & Cao, 2010). There are stiff penalties in place to ensure that these guidelines are followed, with any identified false review incurring penalties of $16,000 per day that the review remains available (Malbon, 2013). However, as has been noted, enforcing these laws is a difficult prospect, as the open and anonymous nature of the Internet and review sites make non-egregious false reviews hard to identify (Malbon, 2013).
It is interesting to note that negatively manipulated reviews or ratings can experience a “correction effect” that will balance a rating over a period of time (Aral, 2014). In fact, in some cases a negative review can actually have an overall positive effect, particularly if the review is perceived as petty or non-relevant to other consumer’s interests (Malbon, 2013).

Chapter 3: Methodology
A cross-sectional survey instrument was developed to determine a relationship between rapidity of response by a business to a false review and the reception of that response. Additionally, the survey attempts to identify factors within a business response that are influential and replicable in eliciting a positive reaction by survey respondents.
The participation target for the survey is a representative U.S. sample of Internet using adults. In order to be statistically significant, the survey needs the participation of at least 500 adults, comprised of men and women ages 18 years of age and older. The margin of error associated with a sample of this size is ±4%.
A simple random sample of men and women was collected using the intercept survey method. This method uses systematized sampling for every third visitor to a targeted webpage and several different types of webpages, e.g. web forums, search engines, and entertainment sites, were utilized.
At the end of a two week period, from April 6th to April 20th, 2014, a sample of 522 adults comprising 236 men and 286 women 18 years of age and older was collected. Non-response surveys were not collected for this sampling.

Chapter 4: Findings
The survey data presented in Figure 1 suggests a strong correlation between the timeliness and the overall reception of the business response. Survey respondents responded most positively the response that was posted within hours of the initial false review. It is also of note that the timeliness of the response was given more weight than the tone or facts presented in the response. Case 2, for example, contained quick response that was somewhat vague and combative, yet it received more positive responses than Case 3, which had more measured and less argumentative response, but was posted a week after the initial review.
Figure 1. Responses by case and type, with the time to response
Reviewers tended to respond positively to supported facts presented by the businesses, and compelling evidence, such as pictures, had greater impact that words alone. Additionally, as mentioned previously, professionalism and courteousness are also an important factor in the reception of the response. Case 1, in which the response was timely, courteous, and direct, received the best response from the survey participants. In contrast, Case 2, in which the response was terse and combative, received the largest number of negative responses.
The survey also highlighted the importance for businesses to have and actively maintain an online presence. 85% of the survey respondents reported using the Internet and online reviews when considering a purchase or making a reservation. This statistic is corroborated by related studies that address Internet and online review site usage (Aral, 2014; Malbon, 2013).
Social media usage also correlates with the extremity of the responses in connection with the likelihood of doing business after reviewing the business responses. In all cases, the participants who used social media infrequently had a more moderated response than those who used social media on a daily basis. The quantitative analysis is presented in Table 1, below.
Table 1
Frequent vs. Infrequent social media user’s likelihood of doing business | | Case 1 | Case 2 | Case 3 | Sex | Likelihood of doing business | Daily | Infrequently | Daily | Infrequently | Daily | Infrequently | F | | 110 | 173 | 110 | 173 | 110 | 173 | | Not at all likely | 1 | 19 | 57 | 13 | 6 | 22 | | Slightly likely | 6 | 16 | 28 | 43 | 15 | 36 | | Moderately likely | 17 | 73 | 12 | 77 | 75 | 73 | | Very likely | 38 | 46 | 8 | 19 | 12 | 21 | | Completely likely | 48 | 19 | 5 | 21 | 2 | 21 | M | | 89 | 150 | 89 | 150 | 89 | 150 | | Not at all likely | 2 | 13 | 44 | 10 | 2 | 27 | | Slightly likely | 6 | 12 | 21 | 27 | 13 | 37 | | Moderately likely | 14 | 72 | 12 | 84 | 61 | 61 | | Very likely | 24 | 36 | 8 | 13 | 8 | 14 | | Completely likely | 43 | 17 | 4 | 16 | 5 | 11 | Grand Total | 199 | 323 | 199 | 323 | 199 | 323 |
Note. Red highlights the lowest number under the 50th percentile, and green the highest. Darkness of shade denotes the extremes.
Chapter 5: Summary, Conclusions, and Recommendations
Online ratings and reviews represent an important aspect of a business’s reputation and success. Businesses that recognize the power and influence of online ratings sites and work to manage their online image over a long period enjoy better ratings in the future, and can charge price premiums versus competing services and businesses. To that end, managing the influence of false reviews and mitigating their business impact should be a core strategy for service industry businesses.
Businesses that are able to respond quickly to false online reviews enjoy higher ratings and better consumer perception. Responding courteously, but directly to false claims with concrete evidence presents one of the best avenues to ensuring that phony negative reviews bear little impact on the business. Additionally, exploring avenues within the social media realm can also be beneficial, as those users are likely to respond in a stronger and more influential fashion than casual users of review sites that do not frequently use social media.

References
Aral, S. (2014). The problem with online ratings. MIT Sloan Management Review, 55(2), 47-52. Retrieved from http://search.proquest.com/docview/1475566579?accountid=7083
Bell, J. E. (2009). Consumers' willingness to depend on user generated content and producer generated content within e-commerce. ProQuest Dissertations and Theses, 98. Retrieved from http://search.proquest.com/docview/304990770?accountid=7083
Chen, Y., & Xie, J. (2005). Third-party product review and firm marketing strategy. Marketing Science, 24(2), 218-240.
Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407-1424.
Dellarocas, C., & Wood, C. A. (2008). The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias. Management Science, 54(3), 460-476.
Forrest, E., & Cao, Y. (2010). Opinions, recommendations and endorsements: The new regulatory framework for social media. Journal of Business and Policy Research, 5, 88–99.
Hennig-Thurau, T., & Walsh, G. (2004). Electronic word of mouth: motives for and consequences of reading customer articulations on the Internet. International Journal of Electronic Commerce, 8(2), 51-74.
Kwon, O., & Sung, Y. (2012). The consumer-generated product review: Its effect on consumers and marketeers. In S. Posavac (Ed), Cracking the Code: Leveraging consumer psychology to drive profitability (pp. 200–18). Armonk: Sharpe.
Lim, E.-P., Nguyen, V.-A., Jindal, N., Liu, B., & Lauw, H. (2010). Detecting product review spammers using rating behaviors. 19th ACM International Conference on Information and Knowledge Management, October 26–30, 393–948.
Malbon, J. (2013). Taking fake online consumer reviews seriously. Journal of Consumer Policy, 36(2), 139-157. doi:http://dx.doi.org/10.1007/s10603-012-9216-7
Moran, R. (2012). "Rateocracy" and corporate reputation. The Futurist, 46(3), 10-13. Retrieved from http://search.proquest.com/docview/1019030725?accountid=7083
Pavlou, P. A., & Dimoka, A. (2006). The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation. Information Systems Research, 17(4), 392-414.
Verhagen, T., Meents, S., & Tan, Y.-H. (2006). Perceived risk and trust associated with purchasing at electronic marketplaces. European Journal of Information Systems, 15, 542–555.

Appendix
-------------------------------------------------
Survey
-------------------------------------------------
Basic Demographics, Review Site and Social Media Usage
BD1: Sex: Male/Female
BD2: Age Range: 18-22 | 23-27 | 28-32 | 33-37 | 38-42 | 43+
BD3: Marital Status: Single | Married | Divorced
SM1: Do you use review sites prior to making a purchase or reservation? Yes/No
SM2: If yes, which of the following review sites do you use most frequently? 1) Yelp 2) TripAdvisor 3) Hotels.com 4) Google Review Listings (via Maps, G+, Search) 5) Other (please specify)
SM3: Are you more likely to purchase a product or make a reservation when you can find additional recommendations online? Yes/No
SM4: Do you use social media? Yes/No
SM5: If yes, how would you describe your general use of social media? 1) Multiple times throughout the day 2) Daily – primarily in the mornings 3) Daily – primarily in the evenings 4) Weekly 5) Less than weekly
-------------------------------------------------
Cases
Case 1 Current state of review: 4.3 of 5
CS1: Initial Reaction to Rating: Positive | Neutral | Negative
Situation: An anonymous user posted a review, claiming that the hotel beds were uncomfortable, the room had an unpleasant odor, and that the hotel was located in a noisy and undesirable area, citing proximity to a firehouse and siren noise as the main culprits. The reviewer gave the hotel a 1 star rating.
Business response: The hotel responded to the review by apologizing for the perceived quality issues. The hotel response then calls into question the validity of the review by providing pictures of their surrounding environment. The hotel response notes that that there are no firehouses or EMS facilities located within a one-mile radius of the hotel.
Response Time of Business: within 12 hours
CS2: Rate the influence of the review:
Not influential | Slightly influential | Moderately influential |
Very influential | Extremely influential
CS3: Rate your reaction to the business response: Positive | Neutral | Negative
CS4: Likelihood of doing business: 1) Not at all likely 2) Slightly likely 3) Moderately likely 4) Very likely 5) Completely likely
Case 2 Current state of review:3 of 5
CS5: Initial Reaction to Rating: Positive | Neutral | Negative
Response Time of Business: 1 day
Situation: This hotel had the service I have ever experienced in my entire life. Arrived early only to be told to move our car from the present position as the receptionist parked there. Once in the room, we found the backroom to be unclean and the towels were like sandpaper. Sorry, but I cannot recommend this place and will not be back.
Business response: I’m not sure which hotel you stayed at, but our receptionists do not have designated parking spots. I am sorry you appear to have experienced poor conditions, but I call into question the validity of this review based on some of the discrepancies between your story and reality.
Response Time of Business:
CS6: Rate the influence of the review:
Not influential | Slightly influential | Moderately influential |
Very influential | Extremely influential
CS7: Rate your reaction to the business response: Positive | Neutral | Negative
CS8: Likelihood of doing business: 1) Not at all likely 2) Slightly likely 3) Moderately likely 4) Very likely 5) Completely likely
Case 3 Current state of review: 3.6 of 5
CS9: Initial Reaction to Rating: Positive | Neutral | Negative
Situation: A review is made stating, “The place is nice but the people who run it are downright rude. I had a friend call from Australia with some big news and after after receiving no response from the room, he was asked by the hotel operator, “do you know what time it is?” 10 pm is hardly late. The operator then told him, “We’re not a call service, you know.” If that is their idea of good service, they are not going to last long. My advice is stay away.
Business response: This is a small hotel, and I (the owner) am the phone operator for our guests. I can state unequivocally that no international calls have been received or handled by me, as the phone switchboard operator. Additionally, as the phone operator, I can assure future guests that a messaging service is present for all of the rooms. If a call is not completed, then the caller can leave a message for the guest.
Response Time of Business: 1 week
CS10: Rate the influence of the review:
Not influential | Slightly influential | Moderately influential |
Very influential | Extremely influential
CS11: Rate your reaction to the response: Positive | Neutral | Negative
CS12: Likelihood of doing business: 1) Not at all likely 2) Slightly likely 3) Moderately likely 4) Very likely 5) Completely likely

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